Metabolic dysregulation impairs lymphocyte function during severe SARS-CoV-2 infection

Cellular metabolic dysregulation is a consequence of SARS-CoV-2 infection that is a key determinant of disease severity. However, how metabolic perturbations influence immunological function during COVID-19 remains unclear. Here, using a combination of high-dimensional flow cytometry, cutting-edge single-cell metabolomics, and re-analysis of single-cell transcriptomic data, we demonstrate a global hypoxia-linked metabolic switch from fatty acid oxidation and mitochondrial respiration towards anaerobic, glucose-dependent metabolism in CD8+Tc, NKT, and epithelial cells. Consequently, we found that a strong dysregulation in immunometabolism was tied to increased cellular exhaustion, attenuated effector function, and impaired memory differentiation. Pharmacological inhibition of mitophagy with mdivi-1 reduced excess glucose metabolism, resulting in enhanced generation of SARS-CoV-2- specific CD8+Tc, increased cytokine secretion, and augmented memory cell proliferation. Taken together, our study provides critical insight regarding the cellular mechanisms underlying the effect of SARS-CoV-2 infection on host immune cell metabolism, and highlights immunometabolism as a promising therapeutic target for COVID-19 treatment.

Some other main points: 1. Age and population demographics of control and patients cohorts are missing and brings into question whether the populations are comparable. A table describing demographic and clinical data of patients is missing, but is absolutely needed. Does disease severity impact metabolic phenotype differently? More information on the patients' disease and inflammatory status at the time-point of analysis would facilitate integration and interpretation of the results. Relevant information would be whether these patients were altogether in a similar stage of the disease, what time-point from diagnosis/onset of the symptoms/positive test the samples were taken or whether they had already developed a specific adaptive immune/antibody response. The authors group all covid-19 patients together, but do not show disease severity stratification.
2. What about gating strategy for flow cytometry? Which kind of controls did the authors used? Which antibodies did they used for staining? The list reported in Table S4 seems incomplete. Did they use FMO controls? The fact that all these information are missing brings into the question whether data are reproducible.
3. Authors claim that hypoxia affects the metabolic phenotype of immune cells. Yet the authors don't show this for their cohort. Therefore, the authors should start by showing this in their cohort of patients.
4. Authors claim that metabolic dysregulation affects immune cells. Supporting information regarding metabolic observation is practically devoid from any experimental observations made by the authors. They base this observation on the mere quantification of GLUT1, and in some cases, DBNG uptake. They do not measure glycolysis nor oxidative phosphorylation by using the Seahorse technology. They do not even mention FAO. 5. The term exhaustion is used throughout the entire manuscript in an improper way. The term " exhaustion" is not supported by the authors findings. Authors did not perform experiments aimed at quantifying functional properties of cells and cell proliferation.
6. The authors claim that mitochondrial dysfunctions are present in several subsets of immune cells, including CD8 T cells and NKT among others. Again, this claim is not supported by experimental evidences other than GSEA analysis. They do not measure/show: i) mitochondrial mass; ii) possible mitochondrial membrane depolarization; iii) oxygen consumption rate; iv) accumulation of dysfunctional mitochondria by using transmission electron microscopy.
7. Mitophagy is indicated as a keyword in this manuscript. Again, claims reporting "mitophagy" are not supported by data. To demonstrate that mitophagy is occurring in cells, gene expression is not sufficient. Protein levels of LC3-I and -II, PINK1, PARKIN, MFN2, FUNDC1, among others should be measured by western blot and the presence of autophagosomes containing mitochondria should be verified by transmission electron microscopy.
8. Correlations to the disease and inflammatory status of the patients at the time-point of analysis are not reported and this questions about the clinical relevance of the paper.
Reviewer #2 (Remarks to the Author): The paper by Gurshaney et al. investigates PBMC from patients with COVID-19 infection for metabolic reprogramming in effector lymphocytes. In addition, the authors us published data to investigate these changes in the lung of COVID-19 patients. The authors claim that hypoxia and anaerobic glycolysis are responsible for lymphocyte dysfunction. This article deals with the of cause timely topic of immune reaction in COVID-19. However, the findings are somehow limited and there might be some over-interpretation in parts of the article. Comments: • The fact that scRNA-seq data is from published data is not properly addressed in several parts of the paper. Figure 1A should make clear that scRNA-seq data is from published datasets. The overview clearly needs to be revised. In addition, it is not clear whether the scRNA-seq data is from Zhang et al. or Liao et al., since both papers are cited, this need clarification in the text.
• The control group in the Liao et al. paper is a shortcoming of that paper, since this was obtained in a different setting and with different techniques. Can the authors confirm their findings using other datasets?
• What is the value of Fig 1G? In Fig 1H, there are too many clusters to use this color code. • Is reduction in CD8 and NK cells due to inflammation per se or a typical finding of COVID-19? • The paper implies that patients with metabolic syndrome and COVID-19 have an alternated immune response to SARS-CoV-2 in comparison to previously heathy persons with COVID-19. Are there any differences between these groups? • The paper nicely shows correlation of marker expression with disease severity. However, the dysfunction itself is not addressed.
Reviewer #3 (Remarks to the Author): The present manuscript by Gurshaney et al. tries to show that hypoxic condition during severe SARS-CoV-2 infection metabolically reprograms the effector lymphocytes to depend on glycolysis for their energy needs. The more dependence glycolysis (and less on fatty acid oxidation) leads to mitochondrial dysfunction, lymphocyte exhaustion, senescence, and hampers memory differentiation. Though several reports so far have been published regarding mitochondrial and T cell exhaustion in COVID 19 disease conditions, evaluating the metabolic phenotype of NKT cells is relatively new in this field. While mostly strong omics-based data acquisition and analysis are presented, a major deficiency of this study is that there is no functional data presented for any cellular subsets (T cells (CD4 or CD8), or NK or NKT). While dependence on glucose is necessary for effector T cells or NK cells function, the authors continue to stress on dysfunctional memory phenotype without showing any alteration in immediate effector functon(with specific cytokine response assays, degranulation, etc.). In absence of any antigen specificity and functional response data on the subsets discussed, the argument about memory dysfunction comes across as very superficial, and such information is necessary to have any meaningful interpretation of the data that has been presented. It must also be noted that multiple papers have now established that even PD1+ T cells (viral-specific too) could exhibit potent effector cytokine and target cytolysis function. The expression of PD1 after the chronic infection has also been argued to limit pathology. Some examples are appended below: PMID: 23644506, PMCID: PMC6112830, PMCID: PMC7406116, PMID: 31591533.
Specific comments and concerns about the study: 1. It is now well established that T cells alter cellular metabolism during activation and clonal expansion. They switch from mitochondrial oxidative phosphorylation to glycolysis to meet their energy demands to differentiate into effector populations during acute infection. Several reports also show that exhausted T cells undergo metabolic reprogramming, including hampered glycolysis accompanied by dysregulated mitochondrial energetics. However, the authors correlated the glycolytic dependence of lymphocytes only with exhaustion phenotype. They might reconsider the premise of their study and perform experiments to distinguish between exhausted and effector phenotypes.
2. The authors here tried to correlate hypoxia with mitochondrial exhaustion. But did not give any evidence to show loss of mitochondrial mass and/or function. So providing microscopic image analysis/ extracellular flux analysis is necessary to prove their point.
3. The expression of surface markers and transcriptional programs of exhausted lymphocytes often overlap with activated cells' phenotype. In particular, since effector T cells transiently express most of the Inhibitory receptors during activation, only checking the expression of inhibitory receptors is insufficient to identify exhausted lymphocytes (mainly T cells). So, showing the evidence of impairment in effector cytokine production and proliferation might be needed side by side to decouple the effector and exhausted phenotype.
4. The authors attempted to demonstrate a diminished memory differentiation in response to SARS-COV2 infection. However, they used the samples taken only at the one-time point when the patients were hospitalized. A longitudinal study looking at the presence of T cells over an extended period would be more appropriate to follow the memory formation.
5. In the methodology section, the authors mentioned that 'Blood samples from hospitalized COVID-19 patients were collected from the Advent Health hospital….' But it is unclear what criteria they used to distinguish between the moderate and severe disease conditions (since all of them were hospitalized).

Rebuttal Letter
We are deeply appreciated for insightful comments from Reviewers. We have carefully addressed these comments in a point-by-point manner throughout the manuscript. The revision was highlighted in blue throughout the manuscript. Please see our answers as bellows. Response: We now provide a detailed gating strategy (Supplementary Fig.1) as well as representative histogram and flow plot throughout the manuscript. We also provide a comprehensive list of all antibodies used in current study (see Supplementary Table 5) 3. Authors claim that hypoxia affects the metabolic phenotype of immune cells. Yet the authors do not show this for their cohort. Therefore, the authors should start by showing this in their cohort of patients.

Response:
We now provide comprehensive patient clinical and demographic information (Supplementary Table 1). We show that 28 out of the 52 COVID-19 patients in our cohort exhibited "severe respiratory impairment", marked by the presence of either respiratory failure, hypoxemia, hypoxia, dependence on supplemental oxygen, dependence on mechanical ventilation, or acute respiratory distress syndrome. Additionally, 41 out of the 52 COVID-19 patients exhibited "dysfunctional lung symptoms" (please see Methods for comprehensive details on patient classification criteria). Thus, it is clear that lung dysfunction is heavily predominant in our cohort.

Authors claim that metabolic dysregulation affects immune cells. Supporting information regarding metabolic observation is practically devoid from any experimental observations made by the authors.
They base this observation on the mere quantification of GLUT1, and in some cases, 2-NBDG uptake. They do not measure glycolysis nor oxidative phosphorylation by using the Seahorse technology. They do not even mention FAO.
Response: Due to the limited amount of blood that we received for each patient and the tremendous degree of lymphopenia, we were unable to sort the requisite number of cells to perform Seahorse assay. Instead, we now used single cell metabolomics assay (SCENITH) to comprehensively measure glycolytic flux, glucose dependence, and AAO and FAO capacities in small abundance subsets (see Figs.2B,D,  Fig.5B, Fig.6D). Additionally, regarding FAO, we now also provide evidence of differential cpt1a expression (see Fig.2F).